Wearable gait analysis technologies have emerged as significant tools in monitoring and preventing gait abnormalities in patients with neurological and musculoskeletal disorders. However, existing gaps related to accuracy, usability, and the transition from monitoring to preventive intervention have been identified. A comprehensive review of the state of wearable devices for gait analysis shows the potential of these technologies, particularly in conditions like Parkinson’s disease and osteoarthritis. The need for reliable, real-time feedback and proactive injury prevention remains underexplored, especially in everyday environments. Several hypothetical solutions to improve wearable technology have been proposed, focusing on enhancing accuracy through multi-sensor integration and utilizing machine learning for predictive injury models. These advancements are designed to overcome the limitations of current devices, particularly the inconsistency in data collection and the lack of real-time intervention systems. The integration of multi-sensor wearable systems with machine learning algorithms is proposed as the best method for closing the gap. These systems allow for precise data collection and predictive modeling, enabling early detection of gait abnormalities and timely interventions. Such systems can offer personalized feedback, enhancing both rehabilitation and injury prevention in real-time. Future directions emphasize the importance of improving usability, conducting larger longitudinal studies, and fully integrating wearable technology into healthcare systems. By advancing wearable gait analysis from passive monitoring to an active preventive tool, significant improvements in patient outcomes and quality of life are anticipated.